Automated analysis of non-stationary machine performance

ABSTRACT

A method for monitoring at least one machine including causing at least a first sensor to acquire at least a first non-stationary signal from at least one machine operating in a non-stationary manner during at least one operational time frame, the at least first sensor providing at least a first non-stationary output, causing at least a second sensor to acquire at least a second non-stationary signal from the at least one machine during the operational time frame, the at least second sensor providing at least a second non-stationary output, fusing the at least first non-stationary output with the at least second non-stationary output to produce a fused output, extracting at least one feature of at least one of the first and second non-stationary signals based on the fused output, analyzing the at least one feature to ascertain a state of health of the at least one machine and performing at least one of a repair operation, maintenance operation and modification of operating parameters of the at least one machine based on the state of health as found by the analyzing.

REFERENCE TO RELATED APPLICATIONS

Reference is hereby made to U.S. Provisional Patent Application Ser. No.62/758,054 filed Nov. 9, 2018 and entitled SYSTEMS AND METHODS FORAUTOMATED DIAGNOSTICS OF NON-STATIONARY MACHINES, the disclosure ofwhich is hereby incorporated by reference and priority of which ishereby claimed.

FIELD OF THE INVENTION

The present invention relates generally to analysis of machineperformance and more particularly to automated analysis of theperformance of non-stationary machines.

BACKGROUND OF THE INVENTION

Various types of systems for automated machine analysis are known in theart.

SUMMARY OF THE INVENTION

The present invention seeks to provide novel systems and methods forhighly accurate automated analysis of the performance of machines havingnon-stationary characteristics, thereby enabling preventativemaintenance and optimization of operational parameters of such machines.

There is thus provided in accordance with a preferred embodiment of thepresent invention a method for monitoring at least one machine includingcausing at least a first sensor to acquire at least a firstnon-stationary signal from at least one machine operating in anon-stationary manner during at least one operational time frame, the atleast first sensor providing at least a first non-stationary output,causing at least a second sensor to acquire at least a secondnon-stationary signal from the at least one machine during theoperational time frame, the at least second sensor providing at least asecond non-stationary output, fusing the at least first non-stationaryoutput with the at least second non-stationary output to produce a fusedoutput, extracting at least one feature of at least one of the first andsecond non-stationary signals based on the fused output, analyzing theat least one feature to ascertain a state of health of the at least onemachine and performing at least one of a repair operation, maintenanceoperation and modification of operating parameters of the at least onemachine based on the state of health as found by the analyzing.

In accordance with a preferred embodiment of the present invention, theat least one feature is insensitive to a level of stationarity of thenon-stationary operation of the machine.

Preferably, the fusing includes modifying the at least firstnon-stationary output based on the at least second non-stationaryoutput.

Preferably, the at least first non-stationary signal represents amechanical state of the machine and the at least second non-stationarysignal represents an operational state of the machine.

In accordance with one preferred embodiment of the present invention,the fusing includes applying a wavelet transform to the first and secondnon-stationary outputs and the modifying includes multiplying thewavelet transform of one of the first and second non-stationary outputsby a binary mask of the wavelet transform of the other one of the firstand second non-stationary outputs.

Additionally or alternatively, the fusing employs deep learning.

Preferably, the deep learning includes combining the at least first andsecond non-stationary outputs into one vector having one or moredimensions and training a neural network to automatically classify thevector.

Preferably, the training of the neural network includes training theneural network to classify the non-stationary outputs based oncorresponding stationary outputs from at least one machine sharing atleast one common characteristic with the at least one machine beingmonitored.

Alternatively, the fusing includes combining the at least first andsecond non-stationary outputs in an interwoven arrangement includingalternating ones of the first and second non-stationary outputs and thedeep learning includes employing an RNN network for time seriesprediction.

Preferably, the extracting includes extracting the at least one featuredirectly from the fused output.

Preferably, the at least one machine includes a group of machinesperforming a joint process.

There is further provided in accordance with another preferredembodiment of the present invention a method for monitoring at least onemachine including causing at least a first sensor to acquire at least afirst non-stationary signal from at least one machine operating in anon-stationary manner during at least one operational time frame, the atleast first sensor providing at, least a first non-stationary output,causing at least a second sensor to acquire at least a secondnon-stationary signal from the machine during the time frame, the atleast second sensor providing at least a second non-stationary output,modifying the at least first non-stationary output based on the at leastsecond non-stationary output to extract at least one feature of thefirst non-stationary output, analyzing the at least one feature toascertain a state of health of the machine and performing at least oneof a repair operation, maintenance operation and modification ofoperating parameters of the machine based on the state of health asfound by the analyzing.

In accordance with a preferred embodiment of the present invention, theat least one feature is insensitive to a level of stationarity of thenon-stationary operation of the machine.

Preferably, the at least first non-stationary signal represents amechanical state of the machine and the at least second non-stationarysignal represents an operational state of the machine.

Preferably, the modifying includes multiplying a wavelet transform ofone of the first and second non-stationary outputs by a binary mask of awavelet transform of the other one of the first and secondnon-stationary outputs.

Additionally or alternatively, the modifying and the analyzing employdeep learning.

Preferably, the deep learning includes combining the at least first andsecond non-stationary outputs into one vector having one or moredimensions and training a neural network to automatically classify thevector.

Preferably, the training of the neural network includes training theneural network to classify the non-stationary outputs based oncorresponding stationary outputs from at least one machine sharing atleast one common characteristic with the at least one machine beingmonitored.

Alternatively, the modifying and the analyzing include combining the atleast first and second non-stationary outputs in an interwovenarrangement including alternating ones of the first and secondnon-stationary outputs and employing an RNN network for time seriesprediction.

Preferably, the at least one machine includes a group of machinesperforming a joint process.

There is further provided in accordance with a further preferredembodiment of the present invention a system for monitoring at least onemachine including a first sensor operative to acquire at least a firstnon-stationary signal from at least one machine operating in anon-stationary manner during at least one operational time frame, the atleast first sensor providing at least a first non-stationary output, asecond sensor operative to acquire at least a second non-stationarysignal from the at least one machine during the operational time frame,the at least second sensor providing at least a second non-stationaryoutput, a signal processor operative to fuse the at least firstnon-stationary output with the at least second non-stationary output toproduce a fused output, a feature extractor operative to extract atleast one feature of at least one of the first and second non-stationarysignals based on the fused output and to analyze the at least onefeature to ascertain a state of health of the at least one machine and amachine control module operative to control the performance of at leastone of a repair operation, maintenance operation and modification ofoperating parameters of the at least one machine based on the state ofhealth.

In accordance with a preferred embodiment of the present invention, theat least one feature is insensitive to a level of stationarity of thenon-stationary operation of the machine.

Preferably, the signal processor is operative to modify the at leastfirst non-stationary output based on the at least second non-stationaryoutput.

Preferably, the at least first non-stationary signal represents amechanical state of the machine and the at least second non-stationarysignal represents an operational state of the machine.

Preferably, the signal processor is operative to apply a wavelettransform to the first and second non-stationary outputs and to multiplythe wavelet transform of one of the first and second non-stationaryoutputs by a binary mask of the wavelet transform of the other one ofthe first and second non-stationary outputs.

Additionally or alternatively, the signal processor is operative toemploy deep learning.

Preferably, the deep learning includes combining the at least first andsecond non-stationary outputs into one vector having one or moredimensions and training a neural network to automatically classify thevector.

Preferably, the training of the neural network includes training theneural network to classify the non-stationary outputs based oncorresponding stationary outputs from at least one machine sharing atleast one common characteristic with the at least one machine beingmonitored.

Alternatively, the signal processor is operative to combine the at leastfirst and second non-stationary outputs in an interwoven arrangementincluding alternating ones of the first and second non-stationaryoutputs and the deep learning includes employing an RNN network for timeseries prediction based on the interwoven arrangement.

Preferably, the at least one feature is directly extracted from thefused output.

Preferably, the at least one machine includes a group of machinesoperative to perform a joint process.

There is also provided in accordance with a still further preferredembodiment of the present invention a system for monitoring at least onemachine including a first sensor operative to acquire at least a firstnon-stationary signal from at least one machine operating in anon-stationary manner during at least one operational time frame, the atleast first sensor providing at least a first non-stationary output, asecond sensor operative to acquire at least a second non-stationarysignal from the machine during the time frame, the at least secondsensor providing at least a second non-stationary output; a signalprocessor operative to modify the at least first non-stationary outputbased on the at least second non-stationary output, to extract at leastone feature of the modified first non-stationary output and to analyzethe at least one feature to ascertain a state of health of the machineand a control module operative to control the performance of at leastone of a repair operation, maintenance operation and modification ofoperating parameters of the machine based on the state of health.

In accordance with a preferred embodiment of the present invention, theat least one feature is insensitive to a level of stationarity of thenon-stationary operation of the machine.

Preferably, the at least first non-stationary signal represents amechanical state of the machine and the at least second non-stationarysignal represents an operational state of the machine.

Preferably, the signal processor is operative to multiply a wavelettransform of one of the first and second non-stationary outputs by abinary mask of a wavelet transform of the other one of the first andsecond non-stationary outputs.

Additionally or alternatively, the signal processor employs deeplearning.

Preferably, the deep learning includes combining the at least first andsecond non-stationary outputs into one vector having one or moredimensions and training a neural network to automatically classify thevector.

Preferably, the training of the neural network includes training theneural network to classify the non-stationary outputs based oncorresponding stationary outputs from at least one machine sharing atleast one common characteristic with the at least one machine beingmonitored.

Alternatively, the signal processor is operative to combine the at leastfirst and second non-stationary outputs in an interwoven arrangementincluding alternating ones of the first and second non-stationaryoutputs and to employ an RNN network for time series prediction based onthe interwoven arrangement.

Preferably, the at least one machine includes a group of machinesperforming a joint process.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be understood and appreciated more fully fromthe following detailed description, taken in conjunction with thedrawings in which:

FIG. 1 is a simplified partially pictorial, partially block-diagramillustration of a system for automated analysis of non-stationarymachine performance, constructed and operative in accordance with apreferred embodiment of the present invention;

FIGS. 2A. 2B and 3 are respective graphs showing features of signalsemanating from a non-stationary machine operating within a system of thetype shown in FIG. 1 ;

FIG. 4 is a simplified partially pictorial, partially block-diagramillustration of a system for automated analysis of non-stationarymachine performance, constructed and operative in accordance withanother preferred embodiment of the present invention;

FIGS. 5A, 5B and 5C are simplified charts respectively illustratingsteps in the training and operation of a machine learning networkforming part of a system of the type shown in FIG. 4 ;

FIG. 6 is a simplified flow chart illustrating steps involved in theextraction of features of signals emanating from a non-stationarymachine operating within a system of the type shown in FIG. 1 or FIG. 4;

FIG. 7 is a simplified schematic illustration of a system for automatedanalysis of non-stationary machine performance, constructed andoperative in accordance with still another preferred embodiment of thepresent invention;

FIGS. 8, 9, 10 and 11 are respective graphs showing features of signalsarising from a non-stationary machine operating in a system of the typeshown in FIG. 7 ;

FIGS. 12A, 12B and 12C are simplified charts respectively illustratingsteps in the training and operation of a machine learning networkforming part of a system of the type shown in FIG. 7 ; and

FIG. 13 is a simplified flow chart combinedly illustrating stepsinvolved in the analysis of features of signals arising from anon-stationary machine operating in a system of the type shown in FIG. 7.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Reference is now made to FIG. 1 , which is a simplified partiallypictorial, partially block-diagram illustration of a system forautomated analysis of non-stationary machine performance, constructedand operative in accordance with a preferred embodiment of the presentinvention.

As seen in FIG. 1 , there is provided a system 100 for monitoring andautomated analysis of the performance of a non-stationary machine, hereembodied, by way of example only, as a motor 102 operating in anon-stationary manner. Motor 102 is preferably a non-stationary machinegenerating non-stationary signals during the operation thereof. As usedherein, the term non-stationary machine refers to a machine performing aprocess having at least one characteristic that changes over time, thusgiving rise to a non-stationary signal during the operation thereof.Such non-stationary machines may include machines driven by a timevarying input, such as, by way of example, a servo motor controlled by adriver modifying the frequency and magnitude of the input power overtime; motors driving moving equipment such as elevators and cranes whichprovide a varying driving power over time; machines experiencing a timevarying load at the output thereof such as CNC process machines subjectto load variations and friction changes during the operation thereof andmachines mounted on a moving base such as, by way of example, motorsinstalled on manufacturing robots.

It is appreciated that conventional machine monitoring and diagnostictechniques are poorly suited for the monitoring and analysis of suchnon-stationary machines, due to the non-stationary signalcharacteristics exhibited thereby. By way of example, conventionalmachine diagnostics based on spectral analysis of vibration signalsgenerated during machine operation are not suitable for analyzingnon-stationary signals due to the time variation of the spectral contentof the non-stationary signals resulting from variation in operatingparameters such as speed and load. By way of example, in the case ofmotor 102, motor 102 may exhibit a non-stationary time varying RPM speedand harmonics thereof. The vibration amplitude of the RPM frequency andits harmonics, which parameter is typically correlated with machinecondition and machine faults, will therefore exhibit time variations andthus cannot be accurately extracted based on conventional spectralanalysis of the sampled signal. Further by way of example, motor 102 mayexhibit non-stationary time varying bearing frequencies or linefrequencies. The vibration amplitude of these frequencies, which aretypically correlated with machine condition and machine faults, willtherefore exhibit time variations and thus cannot be accuratelyextracted based on conventional spectral analysis of the sampled signal.

In accordance with preferred embodiments of the present invention, novelsystems and methods are provided for the monitoring and automatedcondition analysis of non-stationary machines, based on monitoring ofthe non-stationary machines during at least one operational time frameby a plurality of sensors and fusing the respective non-stationaryoutputs of the plurality of sensors into a fused or combined output.Particularly preferably, the fusing includes modifying thenon-stationary signal measured and output by at least one of the sensorsby the non-stationary signal measured and output at least another one ofthe sensors. Such modifying may be used to extract signal features whichmay be accurately analyzed by conventional signal analysis approachessuited for the analysis of stationary signals, without loss of accuracy.Analysis of the signal features may yield the present state of health ofthe machine, which state of health may include information relating tothe machine performance, machine efficiency, developing faults andprediction of future performance of the machine. Additionally oralternatively, such methods may be applied to other types of analysis.

As seen in FIG. 1 , non-stationary machine 102 is preferably monitoredby a plurality of sensors here embodied, by way of example, as a firstvibration sensor 110 measuring vibrations arising from machine 102 and asecond magnetic sensor 112 measuring magnetic flux arising from machine102. It is appreciated that the vibration and magnetic flux signals andarising from machine 102 and measurable by first and second sensors 110and 112 are preferably non-stationary signals due to the inherentnon-stationary manner of operation of machine 102, irrespective of thestate of health of machine 102.

It is appreciated that vibration and magnetic sensors 110 and 112 areshown by way of example only, and that additional or alternative sensortypes may be used to monitor signals emanating from machine 102,including but not limited current sensors, electromagnetic sensors,acoustic sensors, temperature sensors, pressure sensors and fluid flowsensors. It is further appreciated that the plurality of sensorsmonitoring machine 102 may include a plurality of sensors measuring thesame type of signal, such as a plurality of vibration sensors or aplurality of magnetic sensors, wherein the sensors differ from eachother and/or are mounted at different locations with respect to machine102, thus giving rise to slightly different outputs despite measuringthe same type of signal emanating from machine 102.

It is additionally appreciated that the plurality of sensors by whichmachine 102 is monitored may be contact or non-contact sensors. Incertain embodiments of the present invention, the use of non-contactsensors may be preferable due to the difficulty in securely attachingsensors to moving non-stationary equipment.

Plurality of sensors, such as sensors 110 and 112, preferably monitormachine 102 during the same at least one operational time frame ofmachine 102, such that the respective non-stationary outputs of thesensors represent the same period or periods of machine operation. Inone preferred embodiment of the present invention, the plurality ofsensors, such as sensors 110 and 112 monitoring machine 102, are sampledmutually synchronously. The sampling may be synchronous within a timewindow dt of the order of dt≈0.01/f; where f is the frequency ofinterest, such as the RPM frequency. In another preferred embodiment ofthe present invention, the plurality of sensors, such as sensors 110 and112 monitoring machine 102, are sampled mutually synchronously within atime window dt of the order of dt≈0.01/f, where f is the sensor samplingfrequency. By way of example, sensors 110 and 112 may operate with asampling frequency of 20 kHz and thus the time frame dt within which thesensors are synchronously sampled is given by 0.5 μs. It is appreciatedthat the value of 0.01 is chosen here to limit the error in thesynchronous sampling to 1%. However, other values may be selecteddepending on the required error limits.

Preferably, the first non-stationary output provided by vibration sensor110 and the second non-stationary output provided by magnetic sensor 112are provided to respect gain filters (GPs) 114 and 116, which in turnprovide the respective signals to analogue-to-digital converters (ADC)118 and 120. The digitized signals output by ADCs 118 and 120 arepreferably provided to a memory component 122 such as a CPU andoptionally to a cloud database 124. It is understood that thesynchronously sampled data acquired by sensors 110 and 112 may betransferred to an entirely cloud based system, to a local hardwaresystem or to a system comprising both local and cloud based elements,depending on the operating specifications of system 100. It is furtherunderstood that the specific processing components shown herein,including GFs 114 and 116. ADCs 118 and 120 and CPU 122, are detailed byway of example only and that any suitable signal processing componentsmay be employed in systems of the present invention.

The preferably synchronously sampled first and second non-stationaryoutputs respectively output by vibration sensor 110 and magnetic sensor112 are preferably provided to a cross-sensor signal processor 130,which signal processor 130 may be included in cloud database 124 or maycomprise local processing elements.

Preferably, signal processor 130 is operative to receive the first andsecond non-stationary outputs provided by the at least first and secondsensors, here embodied as vibration sensor 110 and vibration sensor 112,and to combine the outputs by modifying the first non-stationary output,for example the output from vibration sensor 110, based at least on thesecond non-stationary output, for example the output from magneticsensor 112, in order to extract at least one feature of the firstnon-stationary output, in this case the vibration output.

Signal processor 130 preferably operates in an automated manner, in onepreferred embodiment of the invention, shown in FIG. 1 , signalprocessor 130 may automatically perform a wavelet analysis on thenon-stationary magnetic and vibration data in order to extract featuresthereof, in another preferred embodiment of the invention, shown inFIGS. 4 and 5 and described herein below, signal processor may employdeep learning in order to extract features of the non-stationary signalsinput thereto.

Signal processor 130 may include a magnetic wavelet calculator 132 and avibration wavelet calculator 134. Magnetic wavelet calculator 132preferably applies a wavelet transform to the non-stationary magneticdata from magnetic sensor 112 in order to extract features of themagnetic data. Vibration wavelet calculator 134 preferably applies awavelet transform to the non-stationary vibration data from, vibrationsensor 110 in order to extract features of the vibration data. Thewavelet transform of the magnetic and vibration data yields the timedependence of the respective magnetic and vibrational spectral content,thereby avoiding the limitations involved in spectral analysis as aresult of the non-stationary nature of the signals generated by machine102. It is appreciated that the magnetic and vibration waveletcalculation performed by magnetic wavelet calculator 132 and vibrationwavelet calculator 134 is a particularly preferred example of anoperation for the extraction of features from the non-stationary dataacquired by sensors 110 and 112. However, it is understood thatalternative data operations in order to extract features of thenon-stationary data acquired by sensors 110 and 112 may be implemented.

A simplified example of a noise-filtered magnetic wavelet image, such asmay be output by magnetic wavelet calculator 132, is shown in FIG. 2A.It is appreciated that in FIG. 2A the magnetic wavelet scale iscalibrated and is plotted with reference to pseudo-frequencies. Signalmagnitude in FIG. 2A is shown with respect to a normalised arbitraryunit scale for clarity of display.

The magnetic wavelet output by the magnetic wavelet calculator 132 ispreferably provided to a mask extractor 138. Mask extractor 138preferably employs image processing algorithms in order to extract amagnetic wavelet mask based on the full magnetic wavelet image butcomprising only the time dependence of certain frequencies, with allother frequencies filtered out. By way of example, the magnetic waveletmask extracted by mask extractor 138 may contain only the timedependence of the RPM frequencies, with all other frequencies filteredout. The magnetic wavelet mask extracted by mask extractor 138 ispreferably a binary wavelet matrix, in which the RPM scales may beconverted to pseudo-frequencies. Exemplary preferred algorithms that maybe used in order to create such a wavelet mask are described inWO2018/198111, assigned to the same assignee as the present inventionand incorporated herein by reference. A simplified example of a binarymagnetic wavelet mask containing only RPM frequencies extracted from thefull magnetic wavelet of FIG. 2A, such as may be output by maskextractor 138, is illustrated in FIG. 2B.

Further by way of example, the magnetic wavelet masked extracted by maskextractor 138 main contain only the time dependence of the RPM frequencyand line frequency and the harmonics thereof. These frequencies maycorrespond to various machine faults, such as mechanical looseness andunbalancing in the case of RPM frequencies and the harmonics thereof andelectrical faults in the case of line frequencies and the harmonicsthereof.

The binary magnetic wavelet mask provided by mask extractor 138 and thefull vibration wavelet provided by vibration wavelet calculator 134 arepreferably provided to a wavelet multiplier 150. Wavelet multiplier 150is preferably operative to multiply the binary magnetic wavelet mask bythe full vibration wavelet image containing all frequencies andamplitudes. The multiplication of the full vibration wavelet by themagnetic wavelet mask serves to filter out those frequencies from thefull vibration wavelet which are not related to the frequency of themask. For example, in the case that the magnetic wavelet mask containsonly RPM frequencies, multiplication of the binary magnetic wavelet maskby the full vibration wavelet yields a vibration wavelet imagecontaining only RPM frequencies. A simplified example of a vibrationwavelet image containing only RPM frequencies, such as may be generatedby wavelet multiplier 150, is illustrated in FIG. 3 .

Further by way of example, in the case that the magnetic wavelet mask isgenerated based on RPM and line frequencies and the harmonics thereof,the mask may be multiplied by the full vibration wavelet. In this case,the mask may be applied to the full vibration wavelet as an inversemask, such that only frequencies other than those obtained bymultiplying the RPM frequencies and line frequencies and the harmonicsthereof by the full vibration wavelet are retained. These frequenciesmay be termed the non-synchronous frequencies and are closely correlatedwith bearing frequencies or frequencies associated with other machinecomponents.

A numerical summation may then be performed on the vibration waveletimage containing only RPM frequencies in order to extract the RPMmagnitude of the vibration signal, in accordance with

A _(vib)(RPM)=ΣM _(mag) ^(w)(f _(rpm) ,t)·V _(vib) ^(w)(f,t)  (1)

wherein M is the binary magnetic wavelet matrix in which the RPMfrequencies are set to one and all other frequencies are set to zero, asoutput by mask extractor 138 and V is the total wavelet transform of thevibration signal, as output by vibration wavelet calculator 134. Thesummation represented in equation (1) may be performed by a featureextractor 152. The product A of equation (1) represents the vibrationamplitude of the RPM frequency. This parameter, as explainedhereinabove, is highly correlated with machine condition and faults. Itis appreciated that feature extractor 152 may be operative to extractany relevant feature from the Filtered vibration wavelet image,including the vibration amplitude of the line frequency or harmonicsthereof. The vibration amplitude of the RPM frequency, as output byfeature extractor 152, may be analyzed in order to ascertain a state ofhealth of machine 102. The results of such analysis may be provided to acontroller 154, based on which controller 154 may automatically modifyan operating parameter of machine 102, initiate or schedule a repairoperation on machine 102 or initiate or schedule a maintenance operationon machine 102. Additionally or alternatively, the results of such ananalysis may be presented in a human-sensible form to at least one humanexpert, based on which the human expert may evaluate the machine stateand initiate maintenance, repair or operational parameter changesaccordingly. Additionally or alternatively, the features extracted maybe tracked over time, for example by feature tracking algorithms.

It is appreciated that the various operations of signal processor 130described herein represent a particularly preferred embodiment ofcomputational operations whereby a first non-stationary signal ismodified based on a second non-stationary signal in order to extract atleast one feature of the first non-stationary output. Here, by way ofexample, the full vibration wavelet derived from the firstnon-stationary vibration signal is modified by the RPM binary maskderived from the second non-stationary magnetic signal, in order toextract the RPM magnitude of the first vibration signal, which RPMmagnitude may be analyzed in order to find the machine state.

It is further appreciated that the modification of the non-stationaryvibration signal by features extracted from the non-stationary magneticsignal in order to derive features of the vibration signal differsfundamentally from a simple comparative analysis of data acquired fromthe vibration and magnetic sensors 110 and 112. In accordance withpreferred embodiments of the present invention, features of data fromone type of sensor, such as magnetic sensor 112, are used as acalibration input for data from another type of sensor, such asvibration sensor 110. This cross-sensor calibration is based on theunderstanding that although the various types of signals, such asvibration and magnetic, are not themselves stationary the relationshipbetween the signals is, such that features from one type of signal maybe used to calibrate another type of signal.

It is understood that the present invention is not limited toapplication to the use of wavelet transforms with respect to aparticular frequency, such as the RPM frequency, but rather may beapplied to other operations with respect to any relevant portion of thenon-stationary signal spectral content. Furthermore, it is understoodthat although in the example described with respect to FIG. 1 the firstsignal, by way of example being a vibration signal, is modified based onfeatures extracted from the second signal, by way of example being amagnetic signal, the inverse is also possible, whereby the secondsignal, by way of example being a magnetic signal, may be modified basedon features extracted from the first signal, by way of example being avibration signal.

It is additionally understood that the various automated signalprocessing components of signal processor 130, including magneticwavelet calculator 132, vibration wavelet calculator 134, mask extractor138, wavelet multiplier 150 and feature extractor 152, are representedherein as individual components for the purpose of ease of explanationof the respective functions thereof. However, the various functionscarried out by components of signal processor 130 are not necessarilyperformed in the order described herein, nor are necessarily sub dividedinto individual separate modules as illustrated herein.

An alternative embodiment of signal processor 130 is shown in FIG. 4 .As seen in FIG. 4 , signal processor 130 may be replaced by a signalprocessor 430 including a vibration-magnetic signal fuser 450 operatingbased on machine learning. Vibration-magnetic signal fuser 450 ispreferably operative to employ machine learning to automatically fusethe non-stationary outputs of vibration and magnetic sensors 110 and112, for example by modifying at least a first non-stationary outputsuch as the non-stationary vibration signal output by vibration sensor110 based on at least a second non-stationary output, such as thenon-stationary magnetic signal output by magnetic sensor 112, so as toextract at least one feature of the first non-stationary output which isinsensitive to a level of stationarity of non-stationary operation ofmachine 102. The at least one feature extracted by vibration-magneticsignal fuser 450 may then be analyzed by conventional signal analysistechniques, which conventional signal analysis techniques are suited forthe analysis of features of stationary signals, in order to yield thecondition of machine 102 and detect anomalies in the operation thereof.It is appreciated that the non-stationary signals emanating from machine102 thus may advantageously be analyzed and the condition of machine 102accurately found based on standard signal analysis techniques,notwithstanding the non-stationary manner of operation of machine 102,due to the extraction of at least one feature of the non-stationaryoutput which is insensitive to the level of stationarity of operation ofmachine 102.

A particularly preferred embodiment of machine learning techniquesemployed in vibration-magnetic signal fuser 450 is illustrated in FIGS.5A-5C. It is appreciated, however, that the machine learning techniquesemployed in vibration-magnetic signal fuser 450 are not limited to thoseillustrated in FIGS. 5A-5C and may include any suitable machine learningtechniques which may be implemented in order to automatically fuse thevarious non-stationary signals emanating from machine 102 so as toextract therefrom signal features insensitive to the level ofstationarity of operation of non-stationary machine 102. The extractedsignal features may then be automatically analyzed using conventionalsignal analysis techniques, which conventional techniques are designedto classify stationary signal features, yet may be accurately applied tothe non-stationary signals emanating from machine 102.

Turning now to FIG. 5A, an initial training step 500 in the operation ofvibration-magnetic signal fuser 450 is shown. A stationary data set 502is preferably provided as an input for initial training step 500. Dataset 502 preferably includes data from a plurality of sensors S₁-S_(n),which plurality of sensors are here embodied, by way of example, asvibration sensor 110 and magnetic sensor 112. It is appreciated,however, that data set 502 may include data from additional oralternative types of sensors, such as current sensors, electromagneticsensors, acoustic sensors, pressure sensors, fluid flow sensors andtemperature sensors. Data set 502 may include data emanating frommachine 102 when operating in a stationary manner and generatingstationary signals. Additionally or alternatively, data set 502 mayinclude data emanating from a group of machines sharing a commoncharacteristic with machine 102 although not necessarily being identicalthereto and operating in a stationary manner to generate stationarysignals. The group of machines sharing a common characteristic fromwhich data set 502 may be acquired may or may not include machine 102.

Plurality of sensors, such as sensors 110 and 112, preferably monitormachine 102 during the same at least one operational time frame ofmachine 102, such that the respective non-stationary outputs of thesensors represent the same period or periods of machine operation. Inone preferred embodiment of the present invention, the plurality ofsensors, such as sensors 110 and 112 monitoring machine 102, are sampledmutually synchronously. The sampling may be synchronous within a timewindow dt of the order of dt≈0.01/f, where f is the frequency ofinterest, such as the RPM frequency. In another preferred embodiment ofthe present invention, the plurality of sensors, such as sensors 110 and112 monitoring machine 102, are sampled mutually synchronously within atime window dt of the order of dt≈0.01/f, where f is the sensor samplingfrequency. By way of example, sensors 110 and 112 may operate with asampling frequency of 20 kHz and thus the time frame dt within which thesensors are synchronously sampled is given by 0.5 μs. It is appreciatedthat the value of 0.01 is chosen here to limit the error in thesynchronous sampling to 1%. However, other values may be selecteddepending on the required error limits.

Data set 502 is preferably provided to a first sensor fusion operator510. First sensor fusion operator 510 preferably combines the data fromsensors 1-n, here embodied as vibration sensor 110 and magnetic sensor112, into a combined single data set. In accordance with one embodimentof the present invention, first sensor fusion operator 510 may combinethe vibration and magnetic signals into a one-dimensional vector, whichvector also includes the logarithmic function of the data, such that thecombined single data set output by first sensor fusion operator 510includes both the original sensor data and the logarithmic functionsthereof. It is appreciated, however, that first sensor fusion operator510 is not limited to including the logarithmic function of the inputsensor data but may additionally or alternatively perform othermathematical operations on the data, including, for example, finding thereciprocal of the data or finding the square of the data. Furthermore,first sensor fusion operator 510 may be operative to fuse the data fromthe plurality of sensors in accordance with other methods as are knownin the an, such as adding the vibration and magnetic data and thelogarithms thereof and creating a two-dimensional vector, wherein thesecond dimension represents the number of sensors, or performing awavelet transform on the vibration and magnetic data.

The combined single data set output by sensor fusion operator 510 ispreferably input to a first neural network NN 1, here indicated byreference number 512. NN1 512 is preferably embodied as a multi-layerperception (MLP) network. NN is preferably operative to automaticallyextract features of the stationary data input thereto, here indicated asX_(s). It is appreciated that the features extracted by NN1 arepreferably based on a fusion of data from sensors 1-n, here embodied asvibration and magnetic sensors 110 and 112. Particularly preferably. NN1may be operative to extract one or more features from one set ofsignals, modify a second set of signals based on the extracted features,and then extract one or more features from the second modified set ofsignals. It is understood that the input of logarithmic data to NN1facilitates the learning by NN1 of multiplication and divisionoperations of the data from the various sensor types with respect toeach other, thus allowing the modification of one signal by features ofanother signal.

The features extracted by NN1 are preferably provided to a firstclassifier, classifier 1, indicated by reference number 514. Classifier1 is preferably operative to classify the features output by NN1 inorder to find the condition of the machine by which the analyzed signalswere generated. The machine condition output by classifier 1 ispreferably provided to a loss calculator 516, whereat a loss function isfound. The loss function may be any suitable loss function. The lossfunction may, by way of example, represent a difference between theclassification of the signal feature provided by classifier 1 and theclassification of the signal feature provided by a human analysist, suchas a cross-entropy loss function.

The loss function if then preferably fed back to NN1 and classifier 1,in order to iteratively further update the weightings within NN1 usingback propagation and gradient descent algorithms, so as to train NN1 andclassifier 1 to more accurately extract relevant features and classifythose features. This iterative process may be continued until the lossfunction found by loss calculator 516 is acceptably low, meaning thatNN1 and classifier 1 are considered to be trained to extract andclassifier signal features of stationary signals with an acceptablelevel of accuracy in comparison to that of a human expert.

By way of example, in the case that the combined single data set outputby first sensor fusion operator 510 comprises a one-dimensional vectorcomprising the signals and the logarithms thereof having a length L, NN1may comprise a fully connected network including several hidden layers,the exact number of which depends on the signal length L and the desirednumber of features to be extracted. The number of neurons in the inputlayer may correspond to the value of L, in the second layer to L/4, inthe third layer to L/10 and in the last layer to L/40, assuming that Lis much greater than 40. A non-linear activation function may beincluded between the layers, such as a Relu or sigmoid function, inorder to allow the network to learn nom-linear relations between thesensor data points.

Further by way of example, in the case that the combined single data setoutput by first sensor fusion operator 510 comprises a two dimensionalvector of vibration and magnetic data and the logarithms thereof havinga total length L>40, then NN1 may be replaced by a convolutional neuralnetwork CNN, for example comprising five kernel filters wherein theinitial two kernels have a wide dimension of length of L/20 to capturelow frequency sensor fusion content and the final three kernels havenarrower dimensions of respective sizes of L/80. L/200 and L/400, tocapture high frequency sensor fusion content. In order to reduce thedimension of the data set, a pooling layer may be included in the CNNbetween the convolution layers having maximum pooling of four and two.The activation function is preferably a Relu. Sigmoid or tan H function,in order to allow the CNN to learn non-linear relations between thesensor data. Following the convolution layers, classifier 1 may beprovided, preferably comprising a fully connected network having a sizeequal to the size of the output layer of the CNN, in order to divide thefeatures into classes. Such classes may represent the severity of aspecific fault in the operation of the machine or the severity of thegeneral state of health of the machine. For example in the case of multiclass classification, a softmax activation function may be included, inthe last layer, whereas in the case of a binary classification, asigmoid function may be used. Batch normalization may be includedbetween layers of the CNN in order to improve the performance of thetraining of the CNN.

Turning now to FIG. 5B, an additional training step 520 in the operationof vibration-magnetic signal fuser 450, preferably subsequent totraining step 500 of FIG. 5A, is shown.

As seen in FIG. 5B, pairs of stationary and non-stationary dataemanating from a given machine such as machine 102 are preferablyprovided. The data pairs preferably include randomly paired stationarydata for machine 102, including stationary data from plurality ofsensors 1-n, and non-stationary data from machine 102, includingnon-stationary data from plurality of sensors 1-n. It is appreciatedthat although the data pairs are randomly paired, it is required thatthe stationary and non-stationary paired data both correspond to thesame state of machine health, which state may be healthy or unhealthy.The pairs are preferably shuffled so as to be mixed, whilst satisfyingthe requirements that members of each pair correspond to the samemachine state of health.

It is appreciated that in order to perform first training step 500 onstationary data and in order to perform second training step 520 onstationary and non-stationary data, it is necessary to distinguishbetween stationary and non-stationary data. In accordance with apreferred embodiment of the present invention, stationary andnon-stationary data may be distinguished between based on stationaritytesting. Such testing may be carried out by a machine learningclassifier, trained to classify the data as stationary or non-stationarybased on data labelling. Alternatively, such testing may be carried outby a signal processing based classifier, operative to analyze theoperational state of the machine from which the data emanated. By way ofexample, such a classifier may be operative to perform a wavelettransform on the magnetic signals in order to ascertain whether thedominant frequency peak is constant or non-constant, thus respectivelyindicating stationary or non-stationary characteristics of machineoperation. Further by way of example, such a classifier may be operativeto analyze the time waveform of the magnetic signal with zero crossingalgorithms in order to ascertain whether the dominant frequency peak isconstant or non-constant, thus respectively indicating stationary ornon-stationary characteristics of machine operation.

Data classified as stationary data is preferably provided to firstsensor fusion operator 510 and data classified as non-stationary data ispreferably provided to a second sensor fusion operator 524. First andsecond sensor fusion operators 510 and 524 preferably respectivelycombine the data input thereto from sensors 1-n, here embodied asvibration sensor 110 and magnetic sensor 112, into a combined singledata set. Furthermore, sensor fusion operators 510 and 524 preferablyrespectively calculate the logarithmic function of data from sensors1-n, such that the combined single data set output by sensor fusionoperators 510 and 524 respectively include both the original sensor dataand the logarithmic functions thereof. It is appreciated that sensorfusion operators 510 and 524 are not limited to calculating thelogarithmic function of the input sensor data but may additionally oralternatively perform other mathematical operations on the data,including, for example, finding the reciprocal of the data or findingthe square of the data. Furthermore, first and second sensor fusionoperators 510 and 524 may be operative to fuse the data from theplurality of sensors in accordance with other methods as are known inthe art, such as adding the vibration and magnetic data and thelogarithms thereof and creating a two-dimensional vector, wherein thesecond dimension represents the number of sensors, or performing awavelet transform on the vibration and magnetic data.

The combined single data set preferably including logarithmic functionsoutput by first sensor fusion operator 510 based on stationary data ispreferably provided to NN1 512. It is understood that NN1 512 is alreadytrained to extract relevant features from the stationary data set outputby first sensor fusion operator 510, as a result of the training thereofin first training step 500 shown in FIG. 5A. Features of the stationarydata extracted by NN1 are here indicated as X_(s).

The combined single data set preferably including logarithmic functionsoutput by second sensor fusion operator 524 based on non-stationary datais preferably provided to a second NN, here indicated as NN 2 530. NN2530 preferably has generally the sane architecture as NN1, but withdifferent weightings within the layers of the NN. NN2 is preferablyoperative to extract relevant, features from the non-stationary data setoutput by second sensor fusion operator 524. It is appreciated that thefeatures extracted by NN2 are preferably based on a fusion ofnon-stationary data from sensors 1-n, here embodied as vibration andmagnetic sensors 110 and 112. It is understood that the input oflogarithmic data to NN2 facilitates the learning by NN2 ofmultiplication and division operations of the data from the varioussensor types with respect to each other. Features of the non-stationarydata set extracted by NN2 are here indicated as X_(us).

In order to train NN2 with respect to NN1, the stationary features X_(s)as found by NN1 and the non-stationary features X_(us) as found by NN2are preferably provided to a loss calculator 532, whereat a lossfunction representing a difference between the stationary andnon-stationary features is found. Any suitable loss function may becalculated by loss calculator 532. Preferably, the loss calculated is aMSE loss, calculated in accordance with (X_(s)−X_(us))².

The loss function if then preferably fed back to NN2, in order tofurther update the weightings within NN2 so as to train NN2 toautomatically accurately extract features from the non-stationary dataset which differ minimally from those features extracted from thestationary data set. This iterative process may be continued until theloss function found by loss calculator 532 is acceptably low, meaningthat NN2 is considered to be trained to extract signal features ofnon-stationary signals with an acceptable level of accuracy incomparison to signal features of stationary signals acquired from thesame machine or a similar group of machines.

Turning now to FIG. 5C, an operational step 540 in the operation ofvibration-magnetic signal fuser 450, preferably subsequent to trainingsteps 500 and 520 of FIGS. 5A and 5B, is shown.

As seen in FIG. 5C, during operation of machine 102 in a non-stationarymanner, non-stationary signals 542 emanating from machine 102 aremeasured by a plurality of sensors S₁-S_(n), here embodied, by way ofexample, as vibration sensor 110 and magnetic sensor 112. Thenon-stationary signals 542 are preferably provided to second sensorfusion operator 524, which provides a combined sensor data set to NN2530. It is understood that NN2 530 is already trained to extractfeatures from non-stationary data in accordance with second trainingstep 520, which features differ from features of stationary data fromthe same or similar machines by a minimal loss function. As a result,features extracted by NN2 from the fused non-stationary sensor signalsmay be provided to classifier 1 514 in order to classify the conditionof machine 102 based thereon, due to the commonality between thesenon-stationary signal features and stationary signal features, based onwhich classifier 1 was trained in first training step 500. It isappreciated that NN2 is thus preferably operative to extract at leastone feature of the first non-stationary output which is insensitive to alevel of stationarity of non-stationary operation of machine 102 andthus may be accurately classified by classifier 1, despite classifier 1having been trained to classify features of stationary signals.

The results of the classification performed by classifier 1 within step540 may be provided to controller 154, based on which controller 154 mayautomatically modify an operating parameter of machine 102, initiate orschedule a repair operation on machine 102 or initiate or schedule amaintenance operation on machine 102. Additionally or alternatively, theresults of such classification may be presented in a human-sensible formto at least one human expert, based on which the human expert mayevaluate the machine state and initiate maintenance, repair oroperational parameter changes accordingly. It is appreciated thatclassifier 1 may be trained in first training step 500 to allow theclassification of specific faults in machine 102, such as, by way ofexample, bearing wear. Additionally or alternatively, classifier 1 maybe trained in a non fault-specific manner, to allow classification ofthe state of machine 102 as healthy or unhealthy, wherein the nature ofthe anomalous operation of machine 102 is not specified by classifier 1.

It is appreciated that in FIGS. 5A 5C, the initial input signals fromplurality of sensors S₁-S_(n) may comprise raw data acquired by theplurality of sensors or may comprise processed data based on the rawdata, in which the raw data is transformed before further processing bythe networks.

Reference is now made to FIG. 6 , which is a simplified flow chartillustrating steps involved in the extraction of features of signalsarising from a non-stationary machine operating within a system of thetype shown in FIG. 1 or in FIG. 4 .

As seen in FIG. 6 , a method 600 for the extraction of features ofsignals arising from a non-stationary machine may begin at a first step601 whereat at least a first and a second set of signals are acquiredfrom a non-stationary machine during the operation thereof.

The first and second sets of signals are preferably acquired during thesame operational at least one time frame of machine 102, such that therespective non-stationary outputs of the sensors represent the sameperiod or periods of machine operation, in one preferred embodiment ofthe present invention, the plurality of sensors, are sampled mutuallysynchronously. The sampling may be synchronous within a time window dtof the order of dt≈0.01/f, where f is the frequency of interest, such asthe RPM frequency. In another preferred embodiment of the presentinvention, the plurality of sensors are sampled mutually synchronouslywithin a time window dr of the order of dt≈0.01/f, where f is the sensorsampling frequency. It is appreciated that the value of 0.01 is chosenhere to limit the error in the synchronous sampling to 1%. However,other values may be selected depending on the required error limits.

The first and second sets of signals preferably have non-stationarycharacteristics due to the non-stationary operation of the machinemonitored thereby.

It is appreciated that the first and second set of signals mayrespectively be different types of signals, such as vibration andmagnetic signals. Alternatively, the first and second set of signals maybe the same type of signals, such as both magnetic signals or bothvibration signals, but acquired by different types of sensors andtherefore differing in certain aspects thereof.

As seen at a second step 604, one or more features are preferablyextracted from the first set of signals. By way of example, one or momfeatures may be extracted from a non-stationary magnetic signal outputby a magnetic sensor or from a non-stationary vibration signal output bya vibration sensor. By way of example, a magnetic wavelet transform maybe applied to a non-stationary magnetic signal in order to derive abinary magnetic wavelet mask containing only frequencies related to theRPM of the non-stationary machine, as described hereinabove withreference to FIG. 1 . Further by way of example, a NN may automaticallyextract one or more features from a non-stationary magnetic signaloutput by a magnetic sensor or from a non-stationary vibration signaloutput by a vibration sensor, as described hereinabove with reference toFIGS. 4-5C.

As seen at a third step 606, the second set of signals is preferablymodified using the features extracted from the first set of signals atsecond step 604. The first set of signals is thus used to convert orcalibrate the second set of signals, prior to the further extraction offeatures from the second set of signals. By way of example, the binarymagnetic wavelet mask derived at second step 604 may be used tocalibrate the vibration signal, by multiplying the binary magneticwavelet mask by a vibration wavelet based on the vibration signal, inorder to filter out all frequencies from the vibration wavelet otherthan those relating to the RPM frequency of the non-stationary machine.Further by way of example, a NN may automatically modify one type ofnon-stationary signal based on features extracted from another type ofnon-stationary signal.

As seen at a fourth step 608, one or more features are then preferablyextracted from the second set of signals as modified at third step 606.By way of example, a summation may be performed over the modifiedvibration wavelet derived at third step 606 in order to extract thevibration amplitude of the RPM frequency. Further by way of example, theNN may automatically extract features of the non-stationary signalmodified thereby. As seen at a fifth step 610, the extracted one or morefeatures may be analyzed in order to find the state of health of thenon-stationary machine being monitored. It is appreciated that the oneor more features extracted by method 600 are preferably representativeof the machine health and are insensitive to the level of stationarityof operation of the machine. By way of example, in the case that thesignal features are extracted by a NN, the extracted features may beclassified and the machine condition hence derived based on using aclassifier trained with data acquired from the same or similar machineoperating in a stationary rather than non-stationary manner.

As seen at a sixth step 612, a repair or maintenance operation may beperformed or scheduled to be performed on the machine or operatingparameters of the machine may be changed, based on the state of healthof the machine ascertained at fifth step 610. It is appreciated thatsixth step 612 may be carried out in an automated manner, by acontroller coupled to the machine being monitored. Additionally oralternatively, the state of health of the machine being monitored may becommunicate to a human expert in human-sensible form, and the humanexpert may be involved in directing repair, maintenance or operationalchanges to the machine being monitored.

Reference is now made to FIG. 7 , which is a simplified partiallypictorial, partially block-diagram illustration of a system forautomated analysis of non-stationary machine performance, constructedand operative in accordance with another preferred embodiment of thepresent invention.

As seen in FIG. 7 , there is provided a system 700 for the monitoringand automated analysis of the performance of a non-stationary machine,here embodied, by way of example only, as a robotic machine 702 or servomotor operating in accordance with a programmable operating regime orrecipe embedded in a machine controller 704. Machine 702 operates in anon-stationary manner due to time varying modification by controller 704of machine parameters such as incoming power and rotation speed.

It is appreciated that conventional machine monitoring and diagnosticsystems are poorly suited for the monitoring and analysis of such anon-stationary machine, due to the non-stationary signal characteristicsexhibited thereby. In accordance with preferred embodiments of thepresent invention, novel systems and methods are provided for themonitoring and automated condition analysis of non-stationary processmachines operating in accordance with a time varying recipe. Suchcondition analysis is preferably based on the monitoring of thenon-stationary machines by a plurality of sensors, fusing thenon-stationary outputs of the sensors over a recipe interval so as tocreate a fused output and extracting signal features reflecting thecondition of the machine operating in accordance with the recipe, basedon the fused output.

In certain embodiments of the present invention, it may be advantageous,following the finding of the recipe interval, to adjust the samplingcharacteristics of the plurality of sensors in accordance with therecipe interval found. Signal features may then be extracted and signalcharacteristics associated with the machine during the recipe intervalautomatically learned. Such learning may be used to build a baselinemodel of machine performance during the recipe interval. Subsequentsignal features extracted from signals generated by the machine may thenbe compared to the baseline model of machine performance, in order todetect anomalies and deviations indicative of changes in the machinestate of health. Appropriate corrective measures, such as machinerepair, maintenance or operating parameter changes may be taken based onthe anomalies found.

As seen in FIG. 7 , robot 702 is preferably monitored by a plurality ofsensors here embodied, by way of example, as a first vibration sensor710 measuring vibrations emanating, for example, from a motor 711driving an arm of robot 702 and a second magnetic sensor 712 measuringmagnetic flux emanating from motor 711. It is appreciated that thevibration and magnetic flux signals and emanating from motor 711 ofrobot 702 and measurable by first and second sensors 710 and 712 arepreferably non-stationary signals due to the non-stationary manner ofoperation of robot 70, irrespective of the state of health of robot 702.

Exemplary data representing vibration and magnetic signals generated bya process machine such as robot 702 as measured by sensors 710 and 712are shown in FIGS. 8 and 9 . FIG. 8 displays data for two vibrationwaveforms during a four second recording of motor 711 of robot 702operating during two different time periods. FIG. 9 displays data fortwo magnetic waveforms acquired synchronously with the two vibrationwaveforms shown in FIG. 8 . As appreciated from consideration of FIGS. 8and 9 , robot 702 is operated in accordance with a recipe characterizedby three operating regions respectively indicated as A, B and C in FIGS.8 and 9 , each of the operating regions having associated therewithincreased vibration and magnetic signals. These three operating regionsA, B and C within the recipe give rise to different signals due todifferent input parameters, as modified by controller 704 of machine702.

It is appreciated that vibration and magnetic sensors 710 and 712 areshown by way of example only, and that additional or alternative sensortypes may be used to monitor the performance of machine 702, includingbut not limited to current sensors, electromagnetic sensors, acousticsensors and temperature sensors. It is further appreciated that theplurality of sensors monitoring machine 702 may include a plurality ofsensors measuring the same type of signal, such as a plurality ofvibration sensors or a plurality of magnetic sensors, wherein thesensors differ from each other, thus giving rise to slightly differentoutputs despite measuring the same parameter of machine 702.

It is additionally appreciated that the plurality of sensors by whichmachine 702 is monitored may be contact or non-contact sensors. Incertain embodiments of the present invention, the use of non-contactsensors may be preferable due to the difficulty in securely attachingsensors to moving non-stationary equipment.

Plurality of sensors, such as sensors 710 and 712, preferably monitormachine 702 during the same at least one operational time frame ofmachine 702, such that the respective non-stationary outputs of thesensors represent the same period or periods of machine operation. Inone preferred embodiment of the present invention, the plurality ofsensors, such as sensors 710 and 712 monitoring machine 702, are sampledmutually synchronously. The sampling may be synchronous within a timewindow dt of the order of dt≈0.01/f, where f is the frequency ofinterest, such as the RPM frequency. In another preferred embodiment ofthe present invention, the plurality of sensors, such as sensors 710 and712 monitoring machine 702, are sampled mutually synchronously within atime window di of the order of dt≈0.01/f, where f is the sensor samplingfrequency. By way of example, sensors 710 and 712 may operate with asampling frequency of 20 kHz and thus the time frame dt within which thesensors are synchronously sampled is given by 0.5 μs. It is appreciatedthat the value of 0.01 is chosen here to limit the error in thesynchronous sampling to 1%. However, other values may be selecteddepending on the required error limits. As is detailed hereinbelow, thesensor sampling frequency may be adjusted in accordance with the recipeinterval following the finding of the recipe interval.

Preferably, the first non-stationary output provided by vibration sensor710 and the second non-stationary output provided by magnetic sensor 712are provided to respect GFs 714 and 716, which in turn provide therespective signals to analogue-to-digital converters (ADC) 718 and 720.The digitized signals output by ADCs 718 and 720 are preferably providedto a memory component 722 such as a CPU and optionally to a clouddatabase 724. It is understood that the synchronously sampled dataacquired by sensors 710 and 712 may be transferred to an entirely cloudbased system, to a local hardware system or to a system comprising bothlocal and cloud based elements, depending on the operatingspecifications of system 700. It is further understood that the specificprocessing components shown herein, including GIs 714 and 716, ADCs 718and 720 and CPU 722, are detailed by way of example only and that anysuitable signal processing components may be employed in systems of thepresent invention.

The synchronously sampled first and second non-stationary outputsrespectively output by vibration sensor 710 and magnetic sensor 712 arepreferably provided to a machine recipe analyzer 730, which machinerecipe analyzer 730 may be included in cloud database 724 or maycomprise local processing elements.

Preferably, machine recipe analyzer 730 includes a recipe intervalfinder 760, operative to find the interval of the recipe governing theoperation of robotic machine 702. Recipe interval finder 760 may findthe recipe interval based on detecting repetitive intervals in theoutputs of sensors 710 and 712. Preferably, although not necessarily,recipe interval finder 760 may employ cross-sensor fusion, as describedhereinabove with reference to FIGS. 1-6 , in order to find repetitivesignal features in fused magnetic and vibration signals generated bymachine 702.

Cross-correlation levels between the two sets of vibration data of FIG.8 , indicating the recipe interval, are shown in FIG. 10 . Across-correlation algorithm may be applied at several time intervals anda grading given based on the correlation magnitude weighted by therotation speed in accordance with

$\begin{matrix}{\frac{1}{S_{tot}}{\int{\frac{1}{W( {f_{rpm},t} )}{{S_{1}^{*}(t)} \cdot {S_{2}( {t + \tau} )}}{dt}}}} & (2)\end{matrix}$

-   -   where S_(i) represents the sensor data from any type of sensor        such as vibration or magnetic, S_(tot) is the sum of the product        of the energy magnitude of the two sensors and W is an RPM        weighted function.

Recipe interval finder 760 may optionally be operatively providefeedback control to sensors 710 and 712, such that followingascertainment of the recipe interval, recipe interval finder 760 may beautomatically operative to reconfigure the sampling parameters ofsensors 710 and 712 in accordance with the recipe interval foundthereby. By way of example, following the ascertainment of the recipeinterval, the sampling duration and/or frequency of one or both ofsensors 710 and 712 may be adjusted in order to allow sensors 710 and712 to monitor robot 702 during the entirety of the recipe interval,during defined periods within the recipe interval or over multiplecycles of the recipe interval.

Following finding of the recipe interval by recipe interval finder 760and optional appropriate corresponding adjustment of sampling parametersof sensors 710 and 712, data acquired by sensors 710 and 712 over therecipe interval may be processed in order to detect zones or regions ofinterest within the signals acquired over the recipe interval, as foundby recipe interval finder 760. Such nines may be selected based on beingparticularly representative of machine performance and may, by way ofexample, correspond to zones including increased vibrations or increasedmagnetic flux signals.

Zone detection may be carried out based on applying a Hilbert transformto data within the recipe interval and analyzing the envelope functionthereof. The Hilbert transform may be applied to data from theindividual sensors or to data extracted from cross-sensor processing ofdata from the individual sensors. Alternatively, zone detection may becarried out based on convolution masking with a Gaussian function, inorder to capture the exponential signal rise and decay associated withvibration zones. An example of a vibration signal divided into zones isshown in FIG. 11 . As seen in FIG. 11 , a dotted line 780 represents thefitting algorithm applied for zone detection and individual zones aredelineated by vertical lines 782.

Data acquired by sensors 710 and 712 over the recipe interval ispreferably analyzed in order to extract characteristic features thereofand thus learn the characteristics of the machine performing the recipeover the recipe interval. In the case that zone detection is performedsignal features may be calculated one a zone by zone basis, per eachspecific zone, rather than over the entire recipe interval. Zones may beclassified based on characteristic features thereof including, by way ofexample, zone energy, zone duration and moment variation within zones,it is appreciated that the division of the recipe interval intorepresentative zones and finding of signal features per zone rather thanover the entire recipe interval is highly advantageous, since featureextraction over the entire recipe interval will typically result in lowresolution poorly representative features due to high variation inoperating regimes within the recipe interval. However, it is understoodthat the division of the recipe interval into zones may not be requiredin certain embodiments of the present invention should variations withinthe recipe interval be of a small enough scale as to render such zonedivision unnecessary.

Additionally or alternatively, analysis of the data acquired by sensors710 and 712 within the recipe interval may be based on other machinelearning algorithms. Features extracted from the data may includestatistical features such as the probability density function moment,cumulants or entropy of the data; operational features such as speed andincoming power; signature features such as principle waveformcomponents, waveform power spectrum, signal energy and signal integrityfeatures; machine performance features such as machine efficiency, forexample based on signal analysis algorithms of the types described inWO/2018/198111; and features associated with environmental featureswhich may be acquired from external sensors, such as environmentaltemperature acquired from an external temperature sensor which may becompared with machine temperature and machine pressure in the case ofmachines involving fluid flow.

Signals features associated with the machine 702 performing the recipe,preferably including zone features, may be learned by recipe learningalgorithms 790. The automated learning of signal features associatedwith the recipe may be augmented by the input of external data such as,by way of example, data from controller 704. Controller data may becorrelated with data from sensors 710 and 712 in order to provideadditional information relating to the recipe according to which machine702 operates and thus improve the accuracy of the detection of therecipe interval.

Following the finding of the recipe characteristics and features byrecipe learning algorithms 790, a model of performance of machine 702during the execution of the recipe thereby may be built. Such a modelmay be constructed by a recipe model builder 792 and may constitute abaseline model representing machine performance within a given reciperegime. The model may be stored in a data base, such as cloud database724. The model may be used as a basis against which subsequent featuresof signals generated by machine 702 during the execution of the recipemay be compared in order to detect anomalies and deviations. By way ofexample, anomalies in machine performance may be detected by an anomalydetector 794. Anomalies in recipe characteristics may be due, forexample, due to machine degradation. Alternatively, anomalies may be dueto malicious intervention in machine operation, for example in the caseof a malicious security breach mounted on controller 704 in order tomodify the machine recipe. Anomalies detected by anomaly detector 794may be output and based on these anomalies, the state of health ofmachine 702 or malicious intervention therewith may be found.Appropriate corrective measures, such as changes in machine operationalparameters, cessation of machine operation or repair of machine may thenbe carried out, either automatically such as by a machine control module796 or based on human intervention.

The model constructed by recipe model builder 792 may include data fromlow power sensors coupled to machine 702, operating at a lower powerthan sensors 710 and 712 but correlated therewith. Such low powersensing may be performed continuously with respect to machine 702,rather than within specific recipe intervals, and may be used to detectreal time anomalies in sensor readings from the model of machineperformance during execution of the recipe. In the case that suchanomalies are found, the lower power sensors may activate the higherpower sensors, such as sensors 710 and 712, in order to collectadditional data based on which more advanced anomaly detectionalgorithms may be applied by anomaly detector 794.

Recipe learning algorithms 790, recipe model builder 792 and anomalydetector 794 correspond to one preferred embodiment of machine learningalgorithms used to automatically build a model of operation of machine702 performing a given recipe and compare signals emanating from machine702 to that model in order to evaluate anomalies in the operation ofmachine 702.

In accordance with a particularly preferred embodiment of the presentinvention, recipe learning algorithms 790, recipe model builder 792 andanomaly detector 794 may employ deep learning, and particularlypreferably may employ a recurrent NN (RNN), such as a LongShort-Term-Memory (LSTM) network in order to find anomalies innon-stationary signals emanating from machine 702 during the executionof a recipe thereby.

A particularly preferred embodiment of a possible implementation of anLSTM network employable within machine recipe analyzer 730 is shown inFIGS. 12A, 12B and 12C. Turning now to FIG. 12A, a non-stationary recipedata set 8100, such as that generated by machine 702, is seen tocomprise a plurality of signals, here embodied as a first signal set S₁comprising vibration signals over time and a second signal set S₂comprising magnetic signals over time. It is appreciated that the timeseries of vibration and magnetic signals preferably spans an entirerecipe interval and may span multiple recipe intervals, as preferablyfound by recipe interval finder 760. It is further appreciated that thefirst and second signal sets S₁ and S₂ may comprise raw sensor data, butmore preferably comprise features extracted from the raw data, such asthe line frequency or RPM or zone features. For example, first set ofsignals S₁ may comprise the vibration levels of motor 711 and second setof signals S₂ may comprise the current line frequencies of the motor,preferably although not necessarily over zones. It is appreciated thatwhereas vibration levels of motor 711 represent a mechanical state ofmotor 711, the line frequencies of the motor 711 based on the magneticsignal directly correlate to the motor speed and thus an operationalstate of the motor.

Signal sets S₁ and S₂ are preferably provided to a sensor-fusiontime-series operator 8102. Sensor-fusion time-series operator 8102preferably fuses the different types of signals into a single combinedsignal comprising alternating ones of the signal types arranged inaccordance with time. Thus, a magnetic signal M_(i-1) is interweavedwith a corresponding synchronous vibration signal V_(i-1) followed by amagnetic signal M_(i) interweaved with a corresponding synchronousvibration signal V_(i) and so forth. In the case that signals S₁ and S₂respectively correspond to signals representing the mechanical andoperational state of the machine, sensor-fusion time-series operator8102 is operative to fuse these signals.

The interwoven data set output by sensor-fusion time-series operator8102, which may comprise interwoven data representing machine mechanicalstate and machine operational state, are preferably provided to an LSTMnetwork 8104. One possible mode of operation of LSTM network 8104 may bebest understood with reference to FIG. 12B, showing a schematicrepresentation of LSTM network 8104. As seen in FIG. 128 . LSTM network8104 receives the interwoven magnetic and vibration signals. At eachneuron 8105, the LSTM network predicts the corresponding shifted signal,such that each LSTM neuron is operative to predict the adjacent nextdata point in the fused time series.

The predicted signal value is then compared to the actual measured valueof that signal at a loss function module 8106. Loss function module 8106is preferably operative to compare the predicted signal value to theactual corresponding measured signal value and iteratively updateweights of the LSTM network 8104 in order to minimize the differencebetween predicted and actual values. The LSTM network 8104 isiteratively trained using back propagation and gradient descentalgorithms on the loss function, which loss function represents thedifference between the time series predicted by the LSTM network 804 andthe true time series. Loss function module 8106 may find, by way ofexample, an MSE loss.

In accordance with another preferred embodiment of the presentinvention, the LSTM network 8104 may be an LSTM autoencoder operative toencode the interwoven data set and output signal features to lossfunction module 8105. Signal features output by LSTM network 8104 arepreferably based on the fusing of the input signals, in this caseembodied as the magnetic and vibration signals, and the extraction offeatures based thereon. Loss function module 8106 is preferablyoperative to compare the decoded signal features output by LSTM network8104 to the input interwoven data set. The difference between thedecoded signal features and the interwoven input data set is then fedback to the LSTM network 8104 and the weights of the encoder iterativelyadjusted with hack propagation and gradient descent algorithms, untilthe loss function is acceptably small and the LSTM network 8104 isconsidered to be trained to encode the interwoven data set to anacceptable level of accuracy.

Turning now to FIG. 12C, following the training of the LSTM network 8104as shown in FIGS. 12A and 128 , a magnetic signal M_(i), generated bymachine 702 during the operation thereof within a recipe interval may beprovided to the trained LSTM network 8104. LSTM network 8104 ispreferably operative to forecast a corresponding vibration signal{circumflex over (V)}_(i) corresponding to the magnetic signal input,based on the learning of the correspondence between the vibration andmagnetic signals in accordance with the process shown in FIGS. 12A and128 .

The forecasted vibration signal {circumflex over (V)}_(i) may becompared to an actual measured vibration signal V_(i) synchronouslymeasured with the magnetic signal M_(i). By way of example, the actualand forecasted vibration signals may be compared by an anomaly detector8108, which anomaly detector 8108 may be a particularly preferredembodiment of anomaly detector 794, in the case that the forecastedsignal differs from the actual measured signal by more than a giventhreshold difference, the measured signal is considered to be anomalousand indicate an unhealthy state of operation of machine 702.

It is appreciated that the systems and methods described herein for theconditional monitoring of a process machine operating in accordance witha given recipe are not limited to the case of a single machine. Rather,the present invention may be implemented with respect to a plurality ofmachines contributing to a common process. Such a process may be aparallel recipe process, in which multiple machines share the sameoperational mode at the same point in time, or a series recipe process,in which machines operate in series and the output of one machine formsthe input of another in a serial manner.

In the case of a multiplicity of machines contributing to a commonprocess, signals are preferably synchronously acquired by a plurality ofsensors coupled to each of a multiplicity of machines. Preferably,recipe features are extracted as described hereinabove in the case of asingle machine 702, including recipe interval, recipe zones and recipezone characteristics, and a recipe baseline model constructed but acrossall or some of the machines carrying out the process rather than for anindividual machine.

In the case of multiple machines, cross-correlation in order toestablish a recipe interval may generally be carried out as describedhereinabove for the case of a single machine, but for the plurality ofsensor across the multiplicity of machines and more specifically forsensors of a given type correspondingly coupled to multiple machines, inaccordance with

$\begin{matrix}{\sum\limits_{i,j}{\frac{1}{S_{tot}^{n}}{\int{\frac{1}{W_{i}( {f_{rpm},t} )}{{S_{i}^{n*}(t)} \cdot {S_{j}^{n}( {t + \tau} )}}{dt}}}}} & (3)\end{matrix}$

where n represents the sensor type for any type of sensor such asvibration or magnetic, i represents the machine index and j runs fromi+1 to i_(n). Other parameters are as described hereinabove withreference to equation (2).

It is appreciated that such a process model for multiple machines may bein addition to a single baseline model built for each machine, such asrobot 702, within the process. This enhances the accuracy by whichanomalies in machine performance may be identified, since multiplemachine process anomaly detection may be used to augment individualmachine anomaly detection.

Sensors of the same or different types respectively coupled to differentmachines of the multiplicity of machines may be correlated. Suchcorrelation may be used to find the type of process being carried out bythe machines and the recipe interval thereof, in order to appropriatelyconfigure sensor sampling parameters, as described hereinabove.

In the case that an LSTM network is employed within machine recipeanalyzer 730 for the analysis of the operation of multiple machinesjointly carrying out a process, signals emanating from the machines maybe combined into, by way of example, a total magnetic vector comprisingmagnetic signals from all of the multiple machines and a total vibrationvector comprising vibration signals from all of the multiple machines,which vibration signals are synchronously acquired with respect to themagnetic signals. The vibration vector may then be interweaved with themagnetic vector by sensor-fusion time-series operator 8102, so as toallow LSTM network 8104 to learn the features of signals of the group ofmachines performing a given recipe and hence detect deviationstherefrom, as described hereinabove. It is appreciated that additionallyor alternatively, signals from multiple machines jointly performing aprocess may be averaged or otherwise combined and then fused bysensor-fusion time-series operator 8102 and provided to LSTM network8104.

Reference is now made to FIG. 13 , which is a simplified flow chartillustrating steps involved in the extraction of features of signalsarising from a non-stationary machine operating in a system of the typeshown in FIG. 7 .

As seen in FIG. 13 , a method 1300 for the extraction of features ofsignals arising from a non-stationary machine carrying out a process inaccordance with a recipe may begin at a first step 1302, wherein signalsare synchronously acquired from a plurality of sensors coupled to atleast one non-stationary machine operating in accordance with a recipe.In accordance with one preferred embodiment of the present invention,the plurality of sensors may be coupled to a single non-stationarymachine executing a recipe embedded in a controller thereof. Inaccordance with another embodiment of the present invention, the atleast one non-stationary machine may comprise a group of non-stationarymachines contributing to a common process, in either a serial orparallel manner and the plurality of sensors may comprise a plurality ofsensors coupled to each machine of the group of non-stationary machines.

As seen at a second step 1304, a recipe interval of the recipe beingexecuted by the machine or group of machines is preferably found. Asseen at a third step 1306, parameters of the plurality of sensors may beadjusted based on the recipe interval found at second step 1304. By wayof example, the sensor sampling frequency or sampling duration may beadjusted so as to allow the sensors to sample the machine or group ofmachines throughout the entirety of the recipe, at given time pointstherewithin or over multiple cycles of the recipe.

As seen at a fourth step 1310, a baseline model of the machineperformance during execution of the recipe may be built based on therecipe characteristics found, it is appreciated that such a baselinemodel may be built for an individual machine performing a process inaccordance with a recipe, for a group of machines contributing to acommon process in accordance with a recipe, or for both.

In accordance with one preferred embodiment of the present invention,building of the model of machine performance may include fusing datafrom the multiple sensors in an interwoven arrangement of the data timepoints for the different sensors and providing the interwoven data setto an LSTM network, for learning the relationship between the datapoints.

As seen at a fifth step 1314, following the creation of a model of themachine performance during execution of the recipe, subsequent machinesignal characteristics measured during performance of the recipe by themachine or group of machines may be compared to the baseline model inorder to detect anomalies with respect thereto. Such anomalies may beindicative of the state of health of the machine or of the securitystatus of the machine.

In the case that an LSTM network is employed at fourth step 1310 inorder to learn the recipe characteristics, fifth step 1314 may involveprediction by the LSTM network of a signal from one type of sensor atone point in time expected to correspond to a measured signal acquiredfrom a different type of sensor at substantially the same point in time.The predicted signal is then compared to an actual measured signal andin the case of deviation between the signals, an anomaly identified.

As seen at an sixth step 1316, in the case that anomalies in machineperformance are detected, actions may be performed with respect to themachine or group of machines, including repair or maintenance activitiesand changes in machine operating parameters including cessation ofoperation of the machine.

It is appreciated that method 1300 is preferably automated, such that amodel of machine performance may be automatically created and anomaliesin machine characteristics with respect thereto may be automaticallydetected, notwithstanding the non-stationary nature of operation of themachine or group of machines being monitored.

It will be appreciated by persons skilled in the art that the presentinvention is not limited to what has been particularly shown anddescribed hereinabove. The scope of the present invention includes bothcombinations and subcombinations of various features describedhereinabove as well as modifications thereof, all of which are not inthe prior art.

1. A method for monitoring at least one machine comprising: causing atleast a first sensor to acquire at least a first non-stationary signalfrom at least one machine operating in a non-stationary manner during atleast one operational time frame, said at least first sensor providingat least a first non-stationary output; causing at least a second sensorto acquire at least a second non-stationary signal from said at leastone machine during said operational time frame, said at least secondsensor providing at least a second non-stationary output; fusing said atleast first non-stationary output with said at least secondnon-stationary output to produce a fused output; extracting at least onefeature of at least one of said first and second non-stationary signalsbased on said fused output; analyzing said at least one feature toascertain a state of health of said at least one machine; and performingat least one of a repair operation, maintenance operation andmodification of operating parameters of said at least one machine basedon said state of health as found by said analyzing.
 2. A method formonitoring at least one machine in accordance with claim 1, wherein saidat least one feature is insensitive to a level of stationarity of saidnon-stationary operation of said machine.
 3. A method for monitoring atleast one machine in accordance with claim 1 or claim 2, wherein saidfusing comprises modifying said at least first non-stationary outputbased on said at least second non-stationary output.
 4. A method formonitoring at least one machine in accordance with any of the precedingclaims, wherein said at least first non-stationary signal represents amechanical state of said machine and said at least second non-stationarysignal represents an operational state of said machine.
 5. A method formonitoring at least one machine in accordance with claim 3 or claim 4,wherein said fusing comprises applying a wavelet transform to said firstand second non-stationary outputs and said modifying comprisesmultiplying said wavelet transform of one of said first and secondnon-stationary outputs by a binary mask of said wavelet transform of theother one of said first and second non-stationary outputs.
 6. A methodfor monitoring at least one machine in accordance with claim 2, whereinsaid fusing employs deep learning.
 7. A method for monitoring at leastone machine in accordance with claim 6, wherein said deep learningcomprises combining said at least first and second non-stationaryoutputs into one vector having one or more dimensions and training aneural network to automatically classify said vector.
 8. A method formonitoring at least one machine in accordance with claim 7, wherein saidtraining said neural network comprises training said neural network toclassify said non-stationary outputs based on corresponding stationaryoutputs from at least one machine sharing at least one commoncharacteristic with said at least one machine being monitored.
 9. Amethod for monitoring at least one machine in accordance with claim 6,wherein said fusing comprises combining said at least first and secondnon-stationary outputs in an interwoven arrangement comprisingalternating ones of said first and second non-stationary outputs andsaid deep learning comprises employing an RNN network for time seriesprediction.
 10. A method according to any of the preceding claims,wherein said extracting comprises extracting said at least one featuredirectly from said fused output.
 11. A method according to any of thepreceding claims, wherein said at least one machine comprises a group ofmachines performing a joint process.
 12. A method for monitoring atleast one machine comprising: causing at least a first sensor to acquireat least a first non-stationary signal from at least one machineoperating in a non-stationary manner during at least one operationaltime frame, said at least first sensor providing at least a firstnon-stationary output; causing at least a second sensor to acquire atleast a second non-stationary signal from said machine during said timeframe, said at least second sensor providing at least a secondnon-stationary output; modifying said at least first non-stationaryoutput based on said at least second non-stationary output to extract atleast one feature of said first non-stationary output; analyzing said atleast one feature to ascertain a state of health of said machine; andperforming at least one of a repair operation, maintenance operation andmodification of operating parameters of said machine based on said stateof health as found by said analyzing.
 13. A method for monitoring atleast one machine in accordance with claim 12, wherein said at least onefeature is insensitive to a level of stationarity of said non-stationaryoperation of said machine;
 14. A method for monitoring at least onemachine in accordance with claim 12 or claim 13, wherein said at leastfirst non-stationary signal represents a mechanical state of saidmachine and said at least second non-stationary signal represents anoperational state of said machine.
 15. A method for monitoring at leastone machine in accordance with claim 12, wherein said modifyingcomprises multiplying a wavelet transform of one of said first andsecond non-stationary outputs by a binary mask of a wavelet transform ofthe other one of said first and second non-stationary outputs.
 16. Amethod for monitoring at least one machine in accordance with claim 12,wherein said modifying and said analyzing employ deep learning.
 17. Amethod for monitoring at least one machine in accordance with claim 16,wherein said deep learning comprises combining said at least first andsecond non-stationary outputs into one vector having one or moredimensions and training a neural network to automatically classify saidvector.
 18. A method for monitoring at least one machine in accordancewith claim 17, wherein said training said neural network comprisestraining said neural network to classify said non-stationary outputsbased on corresponding stationary outputs from at least one machinesharing at least one common characteristic with said at least onemachine being monitored.
 19. A method for monitoring at least onemachine in accordance with claim 16, wherein said modifying and saidanalysing comprise combining said at least first and secondnon-stationary outputs in an interwoven arrangement comprisingalternating ones of said first and second non-stationary outputs andemploying an RNN network for time series prediction.
 20. A method formonitoring at least one machine in accordance with any of claims 12-19,wherein said at least one machine comprises a group of machinesperforming a joint process.
 21. A system for monitoring at least onemachine comprising: a first sensor operative to acquire at least a firstnon-stationary signal from at least one machine operating in anon-stationary manner during at least one operational time frame, saidat least first sensor providing at least a first non-stationary output;a second sensor operative to acquire at least a second non-stationarysignal from said at least one machine during said operational timeframe, said at least second sensor providing at least a secondnon-stationary output; a signal processor operative to fuse said atleast first non-stationary output with said at least secondnon-stationary output to produce a fused output; a feature extractoroperative to extract at least one feature of at least one of said firstand second non-stationary signals based on said fused output and toanalyze said at least one feature to ascertain a state of health of saidat least one machine; and a machine control module operative to controlthe performance of at least one of a repair operation, maintenanceoperation and modification of operating parameters of said at least onemachine based on said state of health.
 22. A system for monitoring atleast one machine in accordance with claim 21, wherein said at least onefeature is insensitive to a level of stationarity of said non-stationaryoperation of said machine.
 23. A system for monitoring at least onemachine in accordance with claim 21 or claim 22, wherein said signalprocessor is operative to modify said at least first non-stationaryoutput based on said at least second non-stationary output.
 24. A systemfor monitoring at least one machine in accordance with any of claims21-23, wherein said at least first non-stationary signal represents amechanical state of said machine and said at least second non-stationarysignal represents an operational state of said machine.
 25. A system formonitoring at least one machine in accordance with claim 23 or claim 24,wherein said signal processor is operative to apply a wavelet transformto said first and second non-stationary outputs and to multiply saidwavelet transform of one of said first and second non-stationary outputsby a binary mask of said wavelet transform of the other one of saidfirst and second non-stationary outputs.
 26. A system for monitoring atleast one machine in accordance with claim 22, wherein said signalprocessor is operative to employ deep learning.
 27. A system formonitoring at least one machine in accordance with claim 26, whereinsaid deep learning comprises combining said at least first and secondnon-stationary outputs into one vector having one or more dimensions andtraining a neural network to automatically classify said vector.
 28. Asystem for monitoring at least one machine in accordance with claim 27,wherein said training said neural network comprises training said neuralnetwork to classify said non-stationary outputs based on correspondingstationary outputs from at least one machine sharing at least one commoncharacteristic with said at least one machine being monitored.
 29. Asystem for monitoring at least one machine in accordance with claim 26,wherein said signal processor is operative to combine said at leastfirst and second non-stationary outputs in an interwoven arrangementcomprising alternating ones of said first and second non-stationaryoutputs and said deep learning comprises employing an RNN network fortime series prediction based on said interwoven arrangement.
 30. Asystem according to any of claims 21-29, wherein said at least onefeature is directly extracted from said fused output.
 31. A systemaccording to any of claims 21-30, wherein said at least one machinecomprises a group of machines operative to perform a joint process. 32.A system for monitoring at least one machine comprising: a first sensoroperative to acquire at least a first non-stationary signal from atleast one machine operating in a non-stationary manner during at leastone operational time frame, said at least first sensor providing atleast a first non-stationary output; a second sensor operative toacquire at least a second non-stationary signal from said machine duringsaid time frame, said at least second sensor providing at least a secondnon-stationary output; a signal processor operative to modify said atleast first non-stationary output based on said at least secondnon-stationary output, to extract at least one feature of said modifiedfirst non-stationary output and to analyze said at least one feature toascertain a state of health of said machine; and a control moduleoperative to control the performance of at least one of a repairoperation, maintenance operation and modification of operatingparameters of said machine based on said state of health.
 33. A systemfor monitoring at least one machine in accordance with claim 32, whereinsaid at least one feature is insensitive to a level of stationarity ofsaid non-stationary operation of said machine;
 34. A system formonitoring at least one machine in accordance with claim 32 or claim 33,wherein said at least first non-stationary signal represents amechanical state of said machine and said at least second non-stationarysignal represents an operational state of said machine.
 35. A system formonitoring at least one machine in accordance with claim 32, whereinsaid signal processor is operative to multiply a wavelet transform ofone of said first and second non-stationary outputs by a binary mask ofa wavelet transform of the other one of said first and secondnon-stationary outputs.
 36. A system for monitoring at least one machinein accordance with claim 32, wherein said signal processor employs deeplearning.
 37. A system for monitoring at least one machine in accordancewith claim 36, wherein said deep learning comprises combining said atleast first and second non-stationary outputs into one vector having oneor more dimensions and training a neural network to automaticallyclassify said vector.
 38. A system for monitoring at least one machinein accordance with claim 37, wherein said training said neural networkcomprises training said neural network to classify said non-stationaryoutputs based on corresponding stationary outputs from at least onemachine sharing at least one common characteristic with said at leastone machine being monitored.
 39. A system for monitoring at least onemachine in accordance with claim 36, wherein said signal processor isoperative to combine said at least first and second non-stationaryoutputs in an interwoven arrangement comprising alternating ones of saidfirst and second non-stationary outputs and to employ an RNN network fortime series prediction based on said interwoven arrangement.
 40. Asystem for monitoring at least one machine in accordance with any ofclaims 32-39, wherein said at least one machine comprises a group ofmachines performing a joint process.